Systems and methods for spectral imaging characterization of macrophages for use in personalization of targeted therapies to prevent fibrosis development in patients with chronic liver disease

Abstract

The present invention includes a method of macrophage phenotype profiling for the assessment, determination, and stratification of risk of development of fibrosis and/or cirrhosis within the liver, and treatment thereof, comprising the steps of: (a) obtaining a liver biopsy sample from a subject; (b) using fluorescently labeled antibodies to analyze the sample, by laboratory assay, for marker identification and expression comparison of one or more macrophage profiling markers relative to the level of expression of a macrophage profiling marker in at least one control or standard sample; and (c) using spectral analysis to correlate the fluorescent signal generated by the macrophage profiling marker/antibody complex to the risk of developing fibrosis and/or cirrhosis, and treating with anti-viral agents or changes in diet, weight loss, or reduction of fat consumption.

What is claimed is: 
     
         1 . A method of macrophage phenotype profiling for determining a risk of developing of fibrosis and/or cirrhosis within the liver, comprising the steps of:
 a) obtaining a liver biopsy sample from a subject;   b) using digital images and a machine learning algorithm to generate a machine learning model that automatically identifies, quantifies, and determines liver fibrosis and/or cirrhosis from a level of expression of macrophage profiling markers and a location of the macrophages expressing CD68, Mac387, and CD163 in the liver biopsy sample by:   c) contacting antibodies attached to fluorescent probes to the liver biopsy sample to detect macrophage profiling markers and imaging multiple regions of interest in the liver biopsy sample to detect a location and a level of expression of the macrophage profiling markers in the liver biopsy sample, wherein the macrophage profiling markers are CD68, Mac387, and CD163, and 4′,6-diamidino-2-phenylindole (DAPI) staining; and   (d) using spectral analysis and the machine learning model to automatically identify, quantify, and correlate from the digital images of the multiple regions of interest in the liver biopsy sample the location and level of expression of the macrophage profiling markers in the liver biopsy sample relative to a control or standard liver biopsy sample from one or more subjects that do not have fibrosis and/or cirrhosis within the liver to calculate the risk of developing fibrosis and/or cirrhosis.   
     
     
         2 . The method of  claim 1 , wherein the macrophage profiling markers are surface bound protein markers, secreted protein markers, mRNA, or combinations thereof. 
     
     
         3 . The method of  claim 2 , wherein the macrophage profiling markers further comprise at least 2, 3, or 4 markers selected from CD206, TGF-β/IL-10, and MMP1. 
     
     
         4 . The method of  claim 1 , wherein the one or more fluorescent probes have a functional group with a spectral emission between 400-800 nm. 
     
     
         5 . The method of  claim 1 , wherein the sample is selected from the group consisting of liver tumor tissue, liver normal tissue, frozen biopsy tissue, paraffin-embedded biopsy tissue, and combinations thereof. 
     
     
         6 . The method of  claim 1 , wherein detection of the macrophage profiling markers is by using spectral imaging microscopy and imaging analysis. 
     
     
         7 . The method of  claim 6 , wherein the spectral imaging microscopy is utilized to image hepatic architecture for the assessment and determination of the location of cells expressing the macrophage profiling markers within a hepatic microenvironment. 
     
     
         8 . The method of  claim 1 , wherein artificial intelligence and/or machine learning is employed to automatically identify, quantify correlate macrophage profiling marker expression levels between samples and controls, standards, or combinations thereof. 
     
     
         9 . The method of  claim 1 , wherein the macrophage profiling markers are at least one of: (1) tolerogenic/anti-inflammatory macrophages that are CD68+/CD16++ or Mac387+/CD16++; or (2) resident pro-inflammatory macrophages characterized by CD68+/CD14++ or Mac387+/CD14++, or both. 
     
     
         10 . The method of  claim 1 , wherein if the subject has fibrosis and a macrophage profiling marker/antibody complex shows CD163+/CD16+, CD68+/Mac387+, and CD68+ macrophages this is indicative of fibrosis due to chronic hepatitis C (HCV+) macrophages; or if the patient has fibrosis and the macrophage profiling marker/antibody complex shows CD163+/Mac387+, CD16+/CD163+/Mac387+, and CD68+ macrophages these patients have non-alcoholic steatohepatitis (NASH) macrophages. 
     
     
         11 . A method of macrophage phenotype profiling for determining a risk of development of fibrosis and/or cirrhosis within the liver, comprising the steps of:
 (a) obtaining an image of a fluorescently labeled liver biopsy sample from a subject;   (b) using spectral analysis and a machine learning algorithm to generate a machine learning model to automatically identify, quantify, and determine liver fibrosis and/or cirrhosis from a level of expression of macrophage profiling markers, wherein the machine learning model analyzes an image of the fluorescently labeled liver biopsy sample compared to a control or standard liver biopsy sample from a subject that does not have a fibrosis and/or cirrhosis within the liver,   wherein the machine learning algorithm compared sample data for NanoString analysis to liver RNA expression data from a PanCancer Immune Profiling Panel and an nCounter Sprint profiler to gene expression levels in the liver RNA expression data;   wherein the liver RNA expression data was normalized and log 2 transformed for genes in the subject with liver fibrosis or cirrhosis and for the control or standard liver biopsy sample; and   wherein the machine learning algorithm identified CD14, CD16, CD68, CD163, and Mac387 as upregulated in liver fibrosis and cirrhosis when compared to the control or standard liver biopsy sample; and   (c) applying the machine learning model to the image of the liver biopsy sample to calculate a relative level of expression and location of the macrophage profiling marker expression levels in the image of the liver biopsy sample, wherein an increase in the expression of CD68, Mac387, and CD163, when compared to the control or standard liver biopsy sample and the location within a hepatic microenvironment is used to detect liver fibrosis and/or cirrhosis.   
     
     
         12 . The method of  claim 11 , wherein the macrophage profiling markers further comprise CD14 and CD16. 
     
     
         13 . The method of  claim 11 , further comprising an anti-inflammatory/restorative markers comprising at least 2, 3, 4, or 5 markers selected from CD206, TGF-β/IL-10, MMP1, and DAPI. 
     
     
         14 . The method of  claim 11 , wherein the macrophage profiling markers are surface bound protein markers, secreted protein markers, mRNA, or combinations thereof. 
     
     
         15 . The method of  claim 11 , wherein the fluorescent probes used to measure macrophage profiling marker expression levels have a functional group with a spectral emission between 400-800 nm. 
     
     
         16 . The method of  claim 11 , wherein the sample is selected from the group consisting of liver tumor tissue, liver normal tissue, frozen biopsy tissue, paraffin-embedded biopsy tissue, and combinations thereof. 
     
     
         17 . The method of  claim 11 , wherein the detection of a macrophage profiling marker/antibody complex is achieved by using spectral imaging microscopy and imaging analysis. 
     
     
         18 . The method of  claim 17 , wherein the spectral imaging microscopy is utilized to conserve the hepatic architecture for the assessment and determination of the location of these cells within the complex microenvironment. 
     
     
         19 . The method of  claim 11 , wherein artificial intelligence and/or machine learning is employed to automatically identify, quantify correlate marker expression levels between samples and controls, standards, or combinations thereof. 
     
     
         20 . The method of  claim 11 , wherein the macrophage profiling marker shows at least one of: (1) tolerogenic/anti-inflammatory macrophages that are CD68+/CD16++ or Mac387+/CD16++; or (2) resident pro-inflammatory macrophages characterized by CD68+/CD14++ or Mac387+/CD14++, or both. 
     
     
         21 . The method of  claim 11 , wherein if the subject has fibrosis and the macrophage profiling markers are: CD163+/CD16+, CD68+/Mac387+, and CD68+ macrophages, this is indicative of fibrosis due to chronic hepatitis C (HCV+) macrophages; or if the patient has fibrosis and the macrophage profiling marker/antibody complex shows CD163+/Mac387+, CD16+/CD163+/Mac387+, and CD68+ macrophages these patients have non-alcoholic steatohepatitis (NASH) macrophages.